"""
Unit Tests for SpatialProcessing Module

Tests spatial processing including observation thinning, super-observations, 
and spatial quality control.
"""

using Test
using Random
using LinearAlgebra
using Statistics

@testset "SpatialProcessing Tests" begin
    
    @testset "Mock Observation Thinning Tests" begin
        @info "Testing mock observation thinning..."
        
        # Create mock observations with spatial distribution
        n_obs = 50
        observations = []
        
        Random.seed!(456)  # For reproducible tests
        
        for i in 1:n_obs
            obs = (
                id = i,
                latitude = 40.0 + 5 * randn(),
                longitude = -75.0 + 5 * randn(), 
                value = 285.0 + 10 * randn(),
                error = 0.5 + 0.3 * rand(),
                quality = 0.5 + 0.5 * rand(),
                time = now() - Minute(rand(1:360))
            )
            push!(observations, obs)
        end
        
        @test length(observations) == n_obs
        
        # Mock spatial thinning algorithm
        function mock_spatial_thinning(obs_list, min_distance_km=25.0)
            # Simple mock: keep observations that are far enough apart
            thinned = []
            
            for obs in obs_list
                keep = true
                for kept_obs in thinned
                    # Simple distance calculation (not great circle, but ok for testing)
                    dlat = obs.latitude - kept_obs.latitude
                    dlon = obs.longitude - kept_obs.longitude
                    distance_deg = sqrt(dlat^2 + dlon^2)
                    distance_km = distance_deg * 111.0  # Rough conversion
                    
                    if distance_km < min_distance_km
                        keep = false
                        break
                    end
                end
                
                if keep
                    push!(thinned, obs)
                end
            end
            
            return thinned
        end
        
        # Test thinning with different distances
        thinned_25km = mock_spatial_thinning(observations, 25.0)
        thinned_50km = mock_spatial_thinning(observations, 50.0)
        
        @test length(thinned_25km) ≤ length(observations)
        @test length(thinned_50km) ≤ length(thinned_25km)  # More aggressive thinning
        @test length(thinned_50km) > 0  # Should keep at least one observation
        
        @info "Thinning results: $(length(observations)) → $(length(thinned_25km)) (25km) → $(length(thinned_50km)) (50km)"
    end
    
    @testset "Mock Super-Observation Creation Tests" begin
        @info "Testing mock super-observation creation..."
        
        # Create clustered observations for super-ob testing
        cluster_centers = [(40.0, -75.0), (41.0, -76.0), (42.0, -74.0)]
        clustered_obs = []
        
        Random.seed!(789)
        
        for (center_lat, center_lon) in cluster_centers
            # Create 5-8 observations around each center
            n_cluster = rand(5:8)
            for i in 1:n_cluster
                obs = (
                    latitude = center_lat + 0.1 * randn(),   # Within ~10km
                    longitude = center_lon + 0.1 * randn(),
                    value = 285.0 + 2 * randn(),  # Similar values
                    error = 0.4 + 0.2 * rand(),
                    quality = 0.8 + 0.2 * rand(),
                    cluster_id = length(cluster_centers)  # Track which cluster
                )
                push!(clustered_obs, obs)
            end
        end
        
        @test length(clustered_obs) >= 15  # At least 5 obs per cluster
        
        # Mock super-observation creation
        function create_mock_super_observations(obs_list, radius_km=20.0)
            super_obs = []
            used = Set{Int}()
            
            for (i, center_obs) in enumerate(obs_list)
                if i in used
                    continue
                end
                
                # Find nearby observations
                nearby = [center_obs]
                nearby_indices = [i]
                
                for (j, obs) in enumerate(obs_list)
                    if j != i && j ∉ used
                        # Simple distance check
                        dlat = obs.latitude - center_obs.latitude
                        dlon = obs.longitude - center_obs.longitude
                        distance_deg = sqrt(dlat^2 + dlon^2)
                        distance_km = distance_deg * 111.0
                        
                        if distance_km <= radius_km
                            push!(nearby, obs)
                            push!(nearby_indices, j)
                        end
                    end
                end
                
                # Create super-observation if we have enough observations
                if length(nearby) >= 3
                    # Quality-weighted average
                    weights = [obs.quality for obs in nearby]
                    weight_sum = sum(weights)
                    
                    super_ob = (
                        latitude = sum(obs.latitude * obs.quality for obs in nearby) / weight_sum,
                        longitude = sum(obs.longitude * obs.quality for obs in nearby) / weight_sum,
                        value = sum(obs.value * obs.quality for obs in nearby) / weight_sum,
                        error = sqrt(sum((obs.error * obs.quality)^2 for obs in nearby)) / weight_sum,
                        quality = sum(weights) / length(nearby),
                        constituent_count = length(nearby),
                        representative_error = 0.1  # Additional representativeness error
                    )
                    
                    push!(super_obs, super_ob)
                    union!(used, nearby_indices)
                end
            end
            
            return super_obs
        end
        
        super_observations = create_mock_super_observations(clustered_obs, 20.0)
        
        @test length(super_observations) > 0
        @test length(super_observations) ≤ length(cluster_centers)  # At most one per cluster
        
        # Validate super-observation properties
        for super_ob in super_observations
            @test super_ob.constituent_count ≥ 3
            @test super_ob.quality > 0
            @test super_ob.error > 0
            @test isfinite(super_ob.latitude)
            @test isfinite(super_ob.longitude)
        end
        
        @info "Created $(length(super_observations)) super-observations from $(length(clustered_obs)) observations"
    end
    
    @testset "Mock Spatial Quality Control Tests" begin
        @info "Testing mock spatial quality control..."
        
        # Create observations with some spatial outliers
        n_good = 20
        n_outliers = 5
        all_obs = []
        
        Random.seed!(101112)
        
        # Good observations clustered around a central location
        for i in 1:n_good
            obs = (
                id = i,
                latitude = 40.0 + 0.5 * randn(),
                longitude = -75.0 + 0.5 * randn(),
                value = 285.0 + 1.0 * randn(),  # Consistent values
                error = 0.5,
                quality = 0.8 + 0.2 * rand(),
                is_outlier = false
            )
            push!(all_obs, obs)
        end
        
        # Add spatial outliers with very different values
        for i in 1:n_outliers
            obs = (
                id = n_good + i,
                latitude = 40.0 + 0.5 * randn(),
                longitude = -75.0 + 0.5 * randn(), 
                value = 285.0 + 10.0 * randn(),  # Much more variable values
                error = 0.5,
                quality = 0.6,
                is_outlier = true
            )
            push!(all_obs, obs)
        end
        
        @test length(all_obs) == n_good + n_outliers
        
        # Mock spatial consistency check
        function check_spatial_consistency(obs_list, neighbor_radius_km=50.0)
            outlier_flags = []
            
            for target_obs in obs_list
                # Find nearby observations
                neighbors = []
                for obs in obs_list
                    if obs.id != target_obs.id
                        dlat = obs.latitude - target_obs.latitude
                        dlon = obs.longitude - target_obs.longitude
                        distance_km = sqrt(dlat^2 + dlon^2) * 111.0
                        
                        if distance_km <= neighbor_radius_km
                            push!(neighbors, obs)
                        end
                    end
                end
                
                # Check consistency with neighbors
                if length(neighbors) >= 3
                    neighbor_values = [obs.value for obs in neighbors]
                    neighbor_mean = mean(neighbor_values)
                    neighbor_std = std(neighbor_values)
                    
                    # Flag as outlier if more than 3 standard deviations from neighbors
                    is_outlier = abs(target_obs.value - neighbor_mean) > 3.0 * neighbor_std
                    
                    push!(outlier_flags, (obs_id=target_obs.id, is_outlier=is_outlier, 
                                        deviation=abs(target_obs.value - neighbor_mean),
                                        neighbor_count=length(neighbors)))
                else
                    # Insufficient neighbors - flag as uncertain
                    push!(outlier_flags, (obs_id=target_obs.id, is_outlier=false,
                                        deviation=0.0, neighbor_count=length(neighbors)))
                end
            end
            
            return outlier_flags
        end
        
        outlier_results = check_spatial_consistency(all_obs, 100.0)  # Large radius to include all
        
        @test length(outlier_results) == length(all_obs)
        
        # Count detected outliers
        detected_outliers = count(result -> result.is_outlier, outlier_results)
        actual_outliers = count(obs -> obs.is_outlier, all_obs)
        
        @info "Outlier detection: $(detected_outliers) detected, $(actual_outliers) actual"
        
        # Should detect at least some outliers (not perfect, but reasonable)
        @test detected_outliers > 0
        
        # Check that all results have reasonable properties
        for result in outlier_results
            @test result.obs_id > 0
            @test result.deviation ≥ 0
            @test result.neighbor_count ≥ 0
        end
    end
    
    @testset "Mock Spatial Grid Operations Tests" begin
        @info "Testing mock spatial grid operations..."
        
        # Create observations on a regular grid
        grid_obs = []
        grid_lats = 35.0:0.5:45.0  # 21 points
        grid_lons = -80.0:0.5:-70.0  # 21 points
        
        for lat in grid_lats[1:2:end]  # Every other point to avoid too many
            for lon in grid_lons[1:2:end]
                obs = (
                    latitude = lat + 0.1 * randn(),  # Add small noise
                    longitude = lon + 0.1 * randn(),
                    value = 285.0 + (lat - 40.0) * 0.5 + randn(),  # Temperature gradient
                    error = 0.5,
                    quality = 0.8 + 0.2 * rand()
                )
                push!(grid_obs, obs)
            end
        end
        
        @test length(grid_obs) > 0
        
        # Mock grid-based processing
        function assign_to_grid_cells(obs_list, grid_spacing_deg=1.0)
            grid_cells = Dict{Tuple{Int, Int}, Vector{Int}}()
            
            for (i, obs) in enumerate(obs_list)
                # Calculate grid cell indices
                lat_idx = round(Int, obs.latitude / grid_spacing_deg)
                lon_idx = round(Int, obs.longitude / grid_spacing_deg)
                cell_key = (lat_idx, lon_idx)
                
                if haskey(grid_cells, cell_key)
                    push!(grid_cells[cell_key], i)
                else
                    grid_cells[cell_key] = [i]
                end
            end
            
            return grid_cells
        end
        
        grid_assignment = assign_to_grid_cells(grid_obs, 1.0)
        
        @test length(grid_assignment) > 0
        
        # Check grid cell contents
        total_assigned = sum(length(indices) for indices in values(grid_assignment))
        @test total_assigned == length(grid_obs)
        
        # Find cells with multiple observations
        multi_obs_cells = filter(pair -> length(pair[2]) > 1, collect(grid_assignment))
        single_obs_cells = filter(pair -> length(pair[2]) == 1, collect(grid_assignment))
        
        @info "Grid assignment: $(length(grid_assignment)) cells, $(length(multi_obs_cells)) multi-obs cells, $(length(single_obs_cells)) single-obs cells"
        
        @test length(multi_obs_cells) + length(single_obs_cells) == length(grid_assignment)
    end
end